A new ice sheet model validated by remote sensing of the Greenland ice sheet

Accurate prediction of future sea level rise requires models that accurately reproduce and explain the recent observed dramatic ice sheet behaviours. This study presents a new multi-phase, multiple-rheology, scalable and extensible geofluid model of the Greenland ice sheet that shows the credential of successfully reproducing the mass loss rate derived from the Gravity Recovery and Climate Experiment (GRACE), and the microwave remote sensed surface melt area over the past decade. Model simulated early 21st century surface ice flow compares satisfactorily with InSAR measurements. Accurate simulation of the three metrics simultaneously cannot be explained by fortunate model tuning and give us confidence in using this modelling system for projection of the future fate of Greenland Ice Sheet (GrIS). Based on this fully adaptable three dimensional, thermo-mechanically coupled prognostic ice model, we examined the flow sensitivity to granular basal sliding, and further identified that this leads to a positive feedback contributing to enhanced mass loss in a future warming climate. The rheological properties of ice depend sensitively on its temperature, thus we further verified modela(sic)Zs temperature solver against in situ observations. Driven by the NCEP/NCAR reanalysis atmospheric parameters, the ice model simulated GrIS mass loss rate compares favourably with that derived from the GRACE measurements, or about -147 km(3)/yr over the 2002-2008 period. Increase of the summer maximum melt area extent (SME) is indicative of expansion of the ablation zone. The modeled SME from year 1979 to 2006 compares well with the cross-polarized gradient ratio method (XPGR) observed melt area in terms of annual variabilities. A high correlation of 0.88 is found between the two time series. In the 30-year model simulation series, the surface melt exhibited large inter-annual and decadal variability, years 2002, 2005 and 2007 being three significant recent melt episodes.